2D geometrically shaped constellations that are simultaneously robust to both residual phase noise (RPN) and AWGN (named as LCM-RPN, where LC is the low-complexity receiver metric from ) for 8 to 64-ary PCAWGN reception using a mismatched PCAWGN model. We added AWGN-only shaped constellations (LCM-AWGN) to serve as a reference; the term M is the modulation cardinality.
Each modulation order is placed in a separate folder, in which, every text file has the coordinates for the in-phase and quadrature components of each symbol in the first and the second column, respectively. The bit mapping for each symbol is natural mapping for the line number, i.e., 000 001 010 011 100 etc.
This database is provided for the Fake News Detection task. In addition to being used in other tasks of detecting fake news, it can be specifically used to detect fake news using the Natural Language Inference (NLI).
This dataset is designed and stored to be compatible for use with both the LIAR test dataset and FakeNewsNet (PolitiFact) datasets as evaluation data. There are two folders, each containing three CSV files.
1- 15212 training samples, 1058 validation samples, and 1054 test samples are the same as (FakeNewsNet PolitiFact) data. The classes of this data are ”real” and ”fake”.
2. 15052 training samples, 1265 validation samples, and 1266 test samples, which is the same as the LIAR test data. The classes in this data are ”pants-fire”, ”false”, and ”barely true”, ”half-true”, ”mostly-true” and ”true”.
The DataSet columns:
id: matches the id in the PolitiFact website API (unique for each sample)
date: The time each article was published in the PolitiFact website
speaker: The person or organization to whom the Statement relates
statement: A claim published in the media by a person or an organization and has been investigated in the PolitiFact article.
sources: The sources used to analyze each Statement
paragraph_based_content: content stored as paragraphed in a list
fullText_based_content: Full text using pasted paragraphs
label: The class for each sample
time-series, accelerometer, gyroscope-to-yaw, acoustic localization